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--- |
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license: apache-2.0 |
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base_model: distilbert-base-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: distilBert_NER_finer |
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results: [] |
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datasets: |
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- nlpaueb/finer-139 |
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language: |
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- en |
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pipeline_tag: token-classification |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# distilBert_NER_finer |
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This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the [Finer-139](https://huggingface.co/datasets/nlpaueb/finer-139) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0198 |
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- Precision: 0.9445 |
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- Recall: 0.9640 |
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- F1: 0.9541 |
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- Accuracy: 0.9954 |
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## Training and evaluation data |
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The training data consists of the 4 most widely available ner_tags from the Finer-139 dataset. The training and the test data were curated from this source accordingly |
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## Prediction procedure |
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``` |
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from transformers import TAutoTokenizer |
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from optimum.onnxruntime import ORTModelForTokenClassification |
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import torch |
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def onnx_inference(checkpoint, test_data, export=False): |
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test_text = " ".join(test_data['tokens']) |
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print("Test Text: " + test_text) |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = ORTModelForTokenClassification.from_pretrained(checkpoint, export=export) |
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inputs = tokenizer(test_text, return_tensors="pt") |
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outputs = model(**inputs).logits |
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predictions = torch.argmax(outputs, dim=2) |
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# Convert each tensor element to a scalar before calling .item() |
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predicted_token_class = [label_list[int(t)] for t in predictions[0]] |
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ner_tags = [label_list[int(t)] for t in test_data['ner_tags']] |
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print("Original Tags: ") |
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print(ner_tags) |
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print("Predicted Tags: ") |
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print(predicted_token_class) |
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onnx_model_path = "" #add the path |
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onnx_inference(onnx_model_path, test_data) |
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""" |
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Here the test_data should contain "tokens" and "ner_tags". This can be of type Dataset. |
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""" |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0034 | 1.0 | 1620 | 0.0261 | 0.9167 | 0.9668 | 0.9411 | 0.9941 | |
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| 0.0031 | 2.0 | 3240 | 0.0182 | 0.9471 | 0.9651 | 0.9561 | 0.9956 | |
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| 0.0012 | 3.0 | 4860 | 0.0198 | 0.9445 | 0.9640 | 0.9541 | 0.9954 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
